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Cygnify · Posted 2mo ago

Full Stack Developer (AI)

On-site
Location
🇸🇬 Singapore
Contract
Full-time
Role
ML engineering
Source
Employer’s own job board (Ashby)

Stack

  • PyTorch
  • LLM
  • SQL
  • Python
  • JavaScript
  • Kubernetes
  • Docker

The posting

Full Stack Engineer – AI

Role

We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users.

Focus

- Build end-to-end product features across frontend, backend, and AI integrations

- Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.

- Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions

- Design real-time AI interactions with streaming, partial results, and tight latency constraints

- Improve system reliability, observability, and fallback mechanisms

- Collaborate closely with ML, backend, and product teams to ship features end-to-end

- Continuously iterate based on real usage and failure modes

Ideal Experiences

- Strong experience in full stack engineering (frontend + backend)

- Solid understanding of system design and API architecture

- Experience working with LLMs, RAG systems, or AI-powered applications

- Ability to handle ambiguity and make pragmatic engineering decisions

- Strong ownership - able to take features from idea to production

- Comfort working in fast-moving environments with evolving requirements

Outcomes

- Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows

- Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions

- Reduce latency and improve responsiveness of AI interactions while maintaining output quality

- Build robust fallback and recovery mechanisms for LLM and tool failures in production environments

- Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring

- Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems

- Contribute to a product experience where AI feels proactive, consistent, and dependable over time

Tech Stack

- Next.js

- Python

- NodeJs

- Pytorch

- OpenAI / Anthropic / open-source LLMs

- SQl & noSQL

- Kubernetes

- Docker